In the framework of the THEIA land data center, we have developed a simple but robust method to map the snow cover from Sentinel-2-like level 2A products. This code was tested with SPOT-4 Take-5 and Landsat-8 series, but it remained to adapt it so that it can run on real Sentinel-2 images! This is now done thanks to Manuel Grizonnet, which allowed us to process the Sentinel-2A image acquired on 06-July-2015 in the Pyrenees as a first example. This image was produced at level 2A by Olivier Hagolle using the MACCS processor. The snow mask from Sentinel-2 images is calculated at 20 m resolution after resampling the green and red bands that are originally at 10 m resolution while the NIR band is at 20 m.

How to make sure everything went well? We can control the snow mask by superposing the mask boundaries on a false color composite:

The Sentinel-2A image of 06-July-2015 (level 2A, tile 30TYN) and its snow mask. The snow mask is in magenta and the background image is a color composite RGB NIR/Red/Green. We also show a zoom in the Vignemale area.

Franck Roux told this sentence in his lecture "Should we be afraid of climate change?" given at the University Paul Sabatier on December 10, 2015 (I quote from memory):

"The human being is a very good weather sensor, but it is a poor climate sensor."

Since our memory can play tricks on us, satellite images are valuable data. As we have seen in a previous article, the snow cover area in the Pyrenees was rather small in January 2016. We can reconstruct the snow extent across the whole mountain range since 2000 with MODIS or even 1998 with SPOT-VGT. However if you want to zoom in on a specific region, the spatial resolution offered by these sensors quickly becomes insufficient so we must turn to the Landsat archive. Continue reading

While CNES is getting ready to produce and distribute Sentinel-2A products obtained with our MACCS processor, I have been asked by impatient users what I thought of SEN2COR Sentinel-2 cloud masks. In this aim, I have downloaded SEN2COR and made a few runs. SEN2COR works on all sorts of multi-platform, and is rather easy to install and to run in its nominal configuration, which is not the case of MACCS, which is intended to be implemented in ground segments, and only works on a Red Hat environment. However, I have been able to process the same date on two sites, and here are the results I obtained.

MACCS

SEN2COR

Comparison of MACCS and SEN2COR cloud masks on a cloud free image of Toulouse. The contours of detected clouds (green), shadows (yellow), water (blue), and snow (pink) are overlayed on the images.Continue reading

peps_download

This is a simple piece of code to automatically download the products provided by the French Sentinel collaborative ground segment named PEPS : https://peps.cnes.fr. PEPS is mirroring all the Sentinel data provided by ESA, and is providing a simplified access.

This code was written thanks to the precious help of one my colleagues at CNES Jérôme Gasperi who developed the "rocket" interface which is used by Peps.

This code relies on python 2.7 and on the curl utility. Because of that, I guess it only works with linux.

The tool is available on my github repository : https://github.com/olivierhagolle/peps_download

Examples

This software is still quite basic, but if you have an account at PEPS, you may download products using command lines like

The first thing we do in the morning at CESBIO is a break : the coffee break. This is the perfect time to gather field information on the state of the snow cover in the Pyrenees. This year everyone agrees that snow is rare ... Can we check this using remote sensing? Continue reading